The present invention related anomaly diagnostic method and their equipment detecting an anomaly early based on a multi-dimensional time series data be output by anomaly sensors, such as plant and equipment, and identifying the sensors related to the detected anomaly.
In power companies, it provides hot water for district heating by utilizing such as waste heat of a gas turbine and provides high-pressure steam and low pressure steam for factories. Petrochemical companies are driving such as gas turbines as power supply equipment. Thus in various plant and equipment using such a gas turbine, the anomaly detection for detecting a malfunction or signs of the equipment is very important to minimize the damage to society.
Not only the gas turbine and steam turbine, water turbine in hydroelectric power plants, nuclear reactors of nuclear power plants, wind power plants of wind turbines, aircraft and heavy equipment engines, railway vehicle and track, escalators, elevators, even in equipment and component level, such as deterioration and life of the installed battery, equipment that require preventive maintenance, such as described above too numerous to mention.
Therefore, the relevant equipment and plant have been carried out to be mounted a plurality of sensors and to be determined whether normal or abnormal in accordance with the monitor criteria of each sensor. U.S. Pat. No. 6,952,662 (Patent Document 1) discloses method for calculating the similarity between observation data and past learning data in a unique way, calculating estimated value by linear combination of the data which are weighted according to the degree of similarity and detecting an anomaly based on the out degree of the observed data. Further, JP 2011-70635 (Patent Document 2) discloses that, in the anomaly detection method for detecting the presence or absence of anomaly based on the anomaly measure calculated by comparison with models created from past normal data, the normal state model is made by Local Subspace Classifier.
However, because the following actions such as measures and studies cannot be determined only by detecting the anomaly, there are needs to diagnose which sensor is associated to anomaly.
As a technology corresponding to these needs, Japanese Patent (Patent Document 3) No. 2012-138044 discloses process state monitor apparatus detecting an anomaly on the basis of the statistics T2 and Q statistic calculated from a plurality of measured variables, and, after the detection, enumerating abnormal factor candidate based on the contribution of each measurement variable for these statistics.